Denoising diffusion probabilistic models (or diffusion models, for short) leverage a Markov process \(q(x_t \mid x_{t-1})\)  to corrupt a data sample \(x_0\)  over \(T\)  discrete timesteps until it is indistinguishable from noise at \(x_T\) . A diffusion model \(p_\xi (x_{t-1} \mid x_t)\)  parameterized by \(\xi \)  is trained to reverse this forward noising process, “denoising” pure noise towards samples that appear drawn from the native data distribution [1]}. Diffusion models were first shown to achieve good generative performance by [2]}; we adapt this framework for generating protein backbones, introducing necessary modifications to work with periodic angular values.
